






'''simple docstring'''





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class  snake_case__      (					UpperCamelCase     ,				unittest.TestCase):
					a_        =					LongformerTokenizer
					a_        =					True
					a_        =					LongformerTokenizerFast
					a_        =					True




					def 		A      (  self  :		Union[str, Any]      )    ->							int:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								UpperCAmelCase_      :       int          =       [
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								]
								UpperCAmelCase_      :       List[Any]          =       dict(zip(_A				, range(len(_A      )      )      )      )
								UpperCAmelCase_      :       List[Any]          =       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								UpperCAmelCase_      :       Optional[Any]          =       {'''unk_token''': '''<unk>'''}

								UpperCAmelCase_      :       List[Any]          =       os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES['''vocab_file''']      )
								UpperCAmelCase_      :       Optional[Any]          =       os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES['''merges_file''']      )
								with open(self.vocab_file				, '''w'''				, encoding='''utf-8'''      ) as fp:
											fp.write(json.dumps(_A      ) + '''\n'''      )
								with open(self.merges_file				, '''w'''				, encoding='''utf-8'''      ) as fp:
											fp.write('''\n'''.join(_A      )      )




					def 		A      (  self  :		int				, **_A  :		List[str]      )    ->							str:
								kwargs.update(self.special_tokens_map      )
								return self.tokenizer_class.from_pretrained(self.tmpdirname				, **_A      )




					def 		A      (  self  :		Optional[Any]				, **_A  :		List[str]      )    ->							List[str]:
								kwargs.update(self.special_tokens_map      )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				, **_A      )




					def 		A      (  self  :		str				, _A  :		Union[str, Any]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       str          =       '''lower newer'''
								UpperCAmelCase_      :       Tuple          =       '''lower newer'''
								return input_text, output_text




					def 		A      (  self  :		int      )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[int]          =       self.tokenizer_class(self.vocab_file				, self.merges_file				, **self.special_tokens_map      )
								UpperCAmelCase_      :       str          =       '''lower newer'''
								UpperCAmelCase_      :       str          =       ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
								UpperCAmelCase_      :       Dict          =       tokenizer.tokenize(_A      )  # , add_prefix_space=True)
								self.assertListEqual(_A				, _A      )

								UpperCAmelCase_      :       Union[str, Any]          =       tokens + [tokenizer.unk_token]
								UpperCAmelCase_      :       Optional[int]          =       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_A      )				, _A      )




					def 		A      (  self  :		str      )    ->							Any:
								UpperCAmelCase_      :       Tuple          =       self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('''Hello world!'''				, add_special_tokens=_A      )				, [0, 3_14_14, 2_32, 3_28, 2]      )
								self.assertListEqual(
								    tokenizer.encode('''Hello world! cécé herlolip 418'''				, add_special_tokens=_A      )				, [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]				, )




					@slow
					def 		A      (  self  :		List[Any]      )    ->							Dict:
								UpperCAmelCase_      :       Dict          =       self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''      )

								UpperCAmelCase_      :       List[Any]          =       tokenizer.encode('''sequence builders'''				, add_special_tokens=_A      )
								UpperCAmelCase_      :       Any          =       tokenizer.encode('''multi-sequence build'''				, add_special_tokens=_A      )

								UpperCAmelCase_      :       int          =       tokenizer.encode(
								    '''sequence builders'''				, add_special_tokens=_A				, add_prefix_space=_A      )
								UpperCAmelCase_      :       Optional[int]          =       tokenizer.encode(
								    '''sequence builders'''				, '''multi-sequence build'''				, add_special_tokens=_A				, add_prefix_space=_A      )

								UpperCAmelCase_      :       Dict          =       tokenizer.build_inputs_with_special_tokens(_A      )
								UpperCAmelCase_      :       Dict          =       tokenizer.build_inputs_with_special_tokens(_A				, _A      )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode




					def 		A      (  self  :		List[str]      )    ->							List[str]:
								UpperCAmelCase_      :       List[str]          =       self.get_tokenizer()

								UpperCAmelCase_      :       Dict          =       '''Encode this sequence.'''
								UpperCAmelCase_      :       int          =       tokenizer.byte_encoder[''' '''.encode('''utf-8'''      )[0]]

								# Testing encoder arguments
								UpperCAmelCase_      :       Dict          =       tokenizer.encode(_A				, add_special_tokens=_A				, add_prefix_space=_A      )
								UpperCAmelCase_      :       Tuple          =       tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
								self.assertNotEqual(_A				, _A      )

								UpperCAmelCase_      :       int          =       tokenizer.encode(_A				, add_special_tokens=_A				, add_prefix_space=_A      )
								UpperCAmelCase_      :       List[Any]          =       tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
								self.assertEqual(_A				, _A      )

								tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}      )
								UpperCAmelCase_      :       List[Any]          =       tokenizer.encode(_A				, add_special_tokens=_A      )
								UpperCAmelCase_      :       Optional[int]          =       tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
								self.assertNotEqual(_A				, _A      )

								# Testing spaces after special tokens
								UpperCAmelCase_      :       Any          =       '''<mask>'''
								tokenizer.add_special_tokens(
								    {'''mask_token''': AddedToken(_A				, lstrip=_A				, rstrip=_A      )}      )  # mask token has a left space
								UpperCAmelCase_      :       str          =       tokenizer.convert_tokens_to_ids(_A      )

								UpperCAmelCase_      :       List[Any]          =       '''Encode <mask> sequence'''
								UpperCAmelCase_      :       int          =       '''Encode <mask>sequence'''

								UpperCAmelCase_      :       List[str]          =       tokenizer.encode(_A      )
								UpperCAmelCase_      :       Optional[Any]          =       encoded.index(_A      )
								UpperCAmelCase_      :       int          =       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
								self.assertEqual(_A				, _A      )

								UpperCAmelCase_      :       int          =       tokenizer.encode(_A      )
								UpperCAmelCase_      :       Any          =       encoded.index(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
								self.assertNotEqual(_A				, _A      )




					def 		A      (  self  :		Optional[Any]      )    ->							Dict:
								pass




					def 		A      (  self  :		int      )    ->							Dict:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
														UpperCAmelCase_      :       Union[str, Any]          =       self.rust_tokenizer_class.from_pretrained(_A				, **_A      )
														UpperCAmelCase_      :       List[Any]          =       self.tokenizer_class.from_pretrained(_A				, **_A      )
														UpperCAmelCase_      :       List[Any]          =       '''A, <mask> AllenNLP sentence.'''
														UpperCAmelCase_      :       List[Any]          =       tokenizer_r.encode_plus(_A				, add_special_tokens=_A				, return_token_type_ids=_A      )
														UpperCAmelCase_      :       Optional[Any]          =       tokenizer_p.encode_plus(_A				, add_special_tokens=_A				, return_token_type_ids=_A      )

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['''token_type_ids''']      )				, sum(tokens_p['''token_type_ids''']      )      )

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['''attention_mask''']      ) / len(tokens_r['''attention_mask''']      )				, sum(tokens_p['''attention_mask''']      ) / len(tokens_p['''attention_mask''']      )				, )

														UpperCAmelCase_      :       Tuple          =       tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
														UpperCAmelCase_      :       Any          =       tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']      )

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['''input_ids''']				, [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
														self.assertSequenceEqual(tokens_r['''input_ids''']				, [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )

														self.assertSequenceEqual(
														    _A				, ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )
														self.assertSequenceEqual(
														    _A				, ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )




					def 		A      (  self  :		Any      )    ->							int:
								for trim_offsets, add_prefix_space in itertools.product([True, False]				, repeat=2      ):
											UpperCAmelCase_      :       int          =       self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname				, use_fast=_A				, add_prefix_space=_A				, trim_offsets=_A      )

											UpperCAmelCase_      :       Dict          =       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
											UpperCAmelCase_      :       Any          =       json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

											self.assertEqual(pre_tokenizer_state['''add_prefix_space''']				, _A      )

											self.assertEqual(post_processor_state['''add_prefix_space''']				, _A      )
											self.assertEqual(post_processor_state['''trim_offsets''']				, _A      )





					def 		A      (  self  :		Any      )    ->							Tuple:
								# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
								# `trim_offsets`
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
														UpperCAmelCase_      :       Dict          =       '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
														UpperCAmelCase_      :       str          =       F"{text_of_1_token} {text_of_1_token}"

														UpperCAmelCase_      :       Dict          =       self.rust_tokenizer_class.from_pretrained(
														    _A				, use_fast=_A				, add_prefix_space=_A				, trim_offsets=_A      )
														UpperCAmelCase_      :       Optional[Any]          =       tokenizer_r(_A				, return_offsets_mapping=_A				, add_special_tokens=_A      )
														self.assertEqual(encoding.offset_mapping[0]				, (0, len(_A      ))      )
														self.assertEqual(
														    encoding.offset_mapping[1]				, (len(_A      ) + 1, len(_A      ) + 1 + len(_A      ))				, )

														UpperCAmelCase_      :       Dict          =       self.rust_tokenizer_class.from_pretrained(
														    _A				, use_fast=_A				, add_prefix_space=_A				, trim_offsets=_A      )
														UpperCAmelCase_      :       str          =       tokenizer_r(_A				, return_offsets_mapping=_A				, add_special_tokens=_A      )
														self.assertEqual(encoding.offset_mapping[0]				, (0, len(_A      ))      )
														self.assertEqual(
														    encoding.offset_mapping[1]				, (len(_A      ) + 1, len(_A      ) + 1 + len(_A      ))				, )

														UpperCAmelCase_      :       Dict          =       self.rust_tokenizer_class.from_pretrained(
														    _A				, use_fast=_A				, add_prefix_space=_A				, trim_offsets=_A      )
														UpperCAmelCase_      :       Union[str, Any]          =       tokenizer_r(_A				, return_offsets_mapping=_A				, add_special_tokens=_A      )
														self.assertEqual(encoding.offset_mapping[0]				, (0, len(_A      ))      )
														self.assertEqual(
														    encoding.offset_mapping[1]				, (len(_A      ), len(_A      ) + 1 + len(_A      ))				, )

														UpperCAmelCase_      :       Tuple          =       self.rust_tokenizer_class.from_pretrained(
														    _A				, use_fast=_A				, add_prefix_space=_A				, trim_offsets=_A      )
														UpperCAmelCase_      :       str          =       tokenizer_r(_A				, return_offsets_mapping=_A				, add_special_tokens=_A      )
														self.assertEqual(encoding.offset_mapping[0]				, (0, len(_A      ))      )
														self.assertEqual(
														    encoding.offset_mapping[1]				, (len(_A      ), len(_A      ) + 1 + len(_A      ))				, )

														UpperCAmelCase_      :       int          =       F" {text}"

														# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
														#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
														# )
														# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
														# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
														# self.assertEqual(
														#     encoding.offset_mapping[1],
														#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
														# )

														UpperCAmelCase_      :       List[str]          =       self.rust_tokenizer_class.from_pretrained(
														    _A				, use_fast=_A				, add_prefix_space=_A				, trim_offsets=_A      )
														UpperCAmelCase_      :       Dict          =       tokenizer_r(_A				, return_offsets_mapping=_A				, add_special_tokens=_A      )
														self.assertEqual(encoding.offset_mapping[0]				, (1, 1 + len(_A      ))      )
														self.assertEqual(
														    encoding.offset_mapping[1]				, (1 + len(_A      ) + 1, 1 + len(_A      ) + 1 + len(_A      ))				, )

														UpperCAmelCase_      :       int          =       self.rust_tokenizer_class.from_pretrained(
														    _A				, use_fast=_A				, add_prefix_space=_A				, trim_offsets=_A      )
														UpperCAmelCase_      :       Optional[Any]          =       tokenizer_r(_A				, return_offsets_mapping=_A				, add_special_tokens=_A      )
														self.assertEqual(encoding.offset_mapping[0]				, (0, 1 + len(_A      ))      )
														self.assertEqual(
														    encoding.offset_mapping[1]				, (1 + len(_A      ), 1 + len(_A      ) + 1 + len(_A      ))				, )

														UpperCAmelCase_      :       Optional[Any]          =       self.rust_tokenizer_class.from_pretrained(
														    _A				, use_fast=_A				, add_prefix_space=_A				, trim_offsets=_A      )
														UpperCAmelCase_      :       Optional[int]          =       tokenizer_r(_A				, return_offsets_mapping=_A				, add_special_tokens=_A      )
														self.assertEqual(encoding.offset_mapping[0]				, (0, 1 + len(_A      ))      )
														self.assertEqual(
														    encoding.offset_mapping[1]				, (1 + len(_A      ), 1 + len(_A      ) + 1 + len(_A      ))				, )










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


_UpperCamelCase						:						Tuple  =					{
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase						:						Optional[int]  =					[
						    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ASTForAudioClassification',
						    'ASTModel',
						    'ASTPreTrainedModel',
						]

try:
						if not is_speech_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase						:						int  =					['ASTFeatureExtractor']

if TYPE_CHECKING:
						from .configuration_audio_spectrogram_transformer import (
						    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ASTConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_audio_spectrogram_transformer import (
												    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ASTForAudioClassification,
												    ASTModel,
												    ASTPreTrainedModel,
												)

						try:
												if not is_speech_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
						import sys

						_UpperCamelCase						:						List[str]  =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


